Transitioning Machine Learning from Theory to Practice in Natural Resources Management

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Sheila M. Saia, Natalie G. Nelson , Anders S. Huseth , Khara Grieger , Brian J. Reich 


Advances in sensing and computation have accelerated at unprecedented rates and scales, in turn creating new opportunities for natural resources managers to improve adaptive and predictive management practices by coupling large environmental datasets with machine learning (ML). Yet, to date, ML models often remain inaccessible to managers working outside of academic research. To identify challenges preventing natural resources managers from putting ML into practice more broadly, we convened a group of 23 stakeholders (i.e., applied researchers and practitioners) who model and analyze data collected from environmental and agricultural systems. Workshop participants shared many barriers regarding their perceptions of, and experiences with, ML modeling. These barriers emphasized three main areas of concern: ML model transparency, availability of educational resources, and the role of process-based understanding in ML model development. Informed by workshop participant input, we offer recommendations on how the ecological modelling community can overcome key barriers preventing ML model use in natural resources management and advance the profession towards data-driven decision-making.



Agriculture, Computer Sciences, Environmental Education, Environmental Sciences, Environmental Studies, Forest Management, Forest Sciences, Natural Resources and Conservation, Natural Resources Management and Policy, Water Resource Management


machine learning, decision-making, natural resources management, stakeholders, decision-support tools, process-based modeling


Published: 2020-10-22 04:21

Last Updated: 2020-10-22 11:21


CC BY Attribution 4.0 International

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There is no data or code associated witht this paper.